PLANET4 aims at filling the gap between scientific research on Artificial Intelligence (AI) and Machine Learning (ML) and its industrial application as enabling technology for the Industry 4.0 paradigm. ML improves the Big Data acquisition and analysis typical of the 4.0 paradigm, leading to the optimization of the industrial processes through fast, lightweight and well-performing algorithms. The academic research efforts on ML have followed a trend of development of complex and resource-intensive algorithms that require cloud-centric architectures while the industrial architectures for Big Data acquisition are in most cases distributed, fragmented and resource-constrained. Recent researches have demonstrated the need of moving toward a decentralized use of ML where algorithms for Big Data acquisition and analysis are executed directly on the machine side (“on the edge”). As this is the future, it becomes evident that a new generation of ML experts, able to adapt these technologies to the industrial needs and to foster their role as the key players of the 4th industrial revolution, is needed.

The PLANET4 project has been designed to enable a knowledge transfer between academia and industry by achieving the following objectives:

a) design and execution of a b-learning course for the porting and integration of ML techniques in I4.0 applications, with particular focus on AI, edge computing and IIOT technologies;

b) formalization and evaluation of a novel method for the description of industrial digitalization needs and pains aimed at enabling fast identification of the most appropriate ML methodologies;

c) formalization of a framework of soft skills and related training materials for 4.0 Innovation and Change Management training workshops, aimed at empowering learners with those transversal skills essential to working in the frame of the 4th industrial revolution;

d) design and development of a portal for the collection and sharing of best practices in the application of ML on the edge for I4.0 applications

The project approach is cross-disciplinary and focuses on both hard skills in ML technologies and soft competencies needed to manage the changes introduced by these technologies in the industrial ecosystem. Moreover, the project will give academics the possibility to gather needs and requirements from the industrial world allowing the adaptation of ML teaching to better fit the real-world industrial pains and needs.